Overview

Dataset statistics

Number of variables26
Number of observations13060
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 MiB
Average record size in memory436.5 B

Variable types

Text3
Categorical1
Numeric22

Alerts

Ave is highly overall correlated with Batting Runs and 5 other fieldsHigh correlation
Batting Runs is highly overall correlated with Ave and 7 other fieldsHigh correlation
Bowling_Runs is highly overall correlated with Cumulative Econ and 7 other fieldsHigh correlation
Ct is highly overall correlated with Cum Inns Total and 3 other fieldsHigh correlation
Cum Inns Total is highly overall correlated with Batting Runs and 5 other fieldsHigh correlation
Cum SR is highly overall correlated with Ave and 4 other fieldsHigh correlation
Cum batting Ave is highly overall correlated with Ave and 6 other fieldsHigh correlation
Cum battings Runs Total is highly overall correlated with Ave and 8 other fieldsHigh correlation
Cumulative Econ is highly overall correlated with Bowling_Runs and 7 other fieldsHigh correlation
Cumulative Inns is highly overall correlated with Bowling_Runs and 7 other fieldsHigh correlation
Cumulative Overs is highly overall correlated with Bowling_Runs and 7 other fieldsHigh correlation
Cumulative Runs is highly overall correlated with Bowling_Runs and 7 other fieldsHigh correlation
Cumulative Wkts is highly overall correlated with Bowling_Runs and 7 other fieldsHigh correlation
D/I is highly overall correlated with Ct and 1 other fieldsHigh correlation
Dis is highly overall correlated with Ct and 3 other fieldsHigh correlation
Econ is highly overall correlated with Bowling_Runs and 6 other fieldsHigh correlation
Inns is highly overall correlated with Ave and 5 other fieldsHigh correlation
Mat is highly overall correlated with Batting Runs and 1 other fieldsHigh correlation
Overs is highly overall correlated with Bowling_Runs and 7 other fieldsHigh correlation
SR is highly overall correlated with Ave and 4 other fieldsHigh correlation
Wkts is highly overall correlated with Bowling_Runs and 6 other fieldsHigh correlation
Inns has 1336 (10.2%) zerosZeros
Batting Runs has 2074 (15.9%) zerosZeros
SR has 2074 (15.9%) zerosZeros
Ave has 2074 (15.9%) zerosZeros
Cum batting Ave has 984 (7.5%) zerosZeros
Cum battings Runs Total has 984 (7.5%) zerosZeros
Cum Inns Total has 602 (4.6%) zerosZeros
Cum SR has 984 (7.5%) zerosZeros
Overs has 4706 (36.0%) zerosZeros
Bowling_Runs has 4705 (36.0%) zerosZeros
Wkts has 6260 (47.9%) zerosZeros
Econ has 4705 (36.0%) zerosZeros
Cumulative Overs has 3742 (28.7%) zerosZeros
Cumulative Wkts has 4750 (36.4%) zerosZeros
Cumulative Runs has 3741 (28.6%) zerosZeros
Cumulative Inns has 3741 (28.6%) zerosZeros
Cumulative Econ has 3741 (28.6%) zerosZeros
Dis has 2401 (18.4%) zerosZeros
Ct has 2435 (18.6%) zerosZeros
St has 12001 (91.9%) zerosZeros
D/I has 2401 (18.4%) zerosZeros

Reproduction

Analysis started2024-11-06 14:22:13.737387
Analysis finished2024-11-06 14:25:09.626948
Duration2 minutes and 55.89 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Player
Text

Distinct3532
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Memory size860.4 KiB
2024-11-06T19:55:10.457262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length10.449234
Min length5

Characters and Unicode

Total characters136467
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1056 ?
Unique (%)8.1%

Sample

1st rowA Ahmadhel
2nd rowA Ahmadhel
3rd rowA Ahmadhel
4th rowA Ahmadhel
5th rowA Ahmadhel
ValueCountFrequency (%)
a 346
 
1.3%
s 343
 
1.3%
khan 286
 
1.1%
r 243
 
0.9%
m 224
 
0.8%
ali 213
 
0.8%
mohammad 209
 
0.8%
d 201
 
0.8%
singh 188
 
0.7%
j 188
 
0.7%
Other values (3717) 24072
90.8%
2024-11-06T19:55:12.052480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 16802
 
12.3%
13453
 
9.9%
i 7163
 
5.2%
e 7014
 
5.1%
n 6687
 
4.9%
r 6306
 
4.6%
h 5917
 
4.3%
l 4711
 
3.5%
o 4450
 
3.3%
s 4152
 
3.0%
Other values (45) 59812
43.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 136467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 16802
 
12.3%
13453
 
9.9%
i 7163
 
5.2%
e 7014
 
5.1%
n 6687
 
4.9%
r 6306
 
4.6%
h 5917
 
4.3%
l 4711
 
3.5%
o 4450
 
3.3%
s 4152
 
3.0%
Other values (45) 59812
43.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 136467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 16802
 
12.3%
13453
 
9.9%
i 7163
 
5.2%
e 7014
 
5.1%
n 6687
 
4.9%
r 6306
 
4.6%
h 5917
 
4.3%
l 4711
 
3.5%
o 4450
 
3.3%
s 4152
 
3.0%
Other values (45) 59812
43.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 136467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 16802
 
12.3%
13453
 
9.9%
i 7163
 
5.2%
e 7014
 
5.1%
n 6687
 
4.9%
r 6306
 
4.6%
h 5917
 
4.3%
l 4711
 
3.5%
o 4450
 
3.3%
s 4152
 
3.0%
Other values (45) 59812
43.8%

Season
Categorical

Distinct41
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size797.5 KiB
2024
1267 
2022
1080 
2023
951 
2023/24
890 
2021/22
827 
Other values (36)
8045 

Length

Max length7
Median length7
Mean length5.5190658
Min length4

Characters and Unicode

Total characters72079
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019/20
2nd row2020
3rd row2020/21
4th row2021
5th row2023

Common Values

ValueCountFrequency (%)
2024 1267
 
9.7%
2022 1080
 
8.3%
2023 951
 
7.3%
2023/24 890
 
6.8%
2021/22 827
 
6.3%
2022/23 794
 
6.1%
2019/20 714
 
5.5%
2019 637
 
4.9%
2021 611
 
4.7%
2018/19 364
 
2.8%
Other values (31) 4925
37.7%

Length

2024-11-06T19:55:12.567522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024 1267
 
9.7%
2022 1080
 
8.3%
2023 951
 
7.3%
2023/24 890
 
6.8%
2021/22 827
 
6.3%
2022/23 794
 
6.1%
2019/20 714
 
5.5%
2019 637
 
4.9%
2021 611
 
4.7%
2018/19 364
 
2.8%
Other values (31) 4925
37.7%

Most occurring characters

ValueCountFrequency (%)
2 27147
37.7%
0 16349
22.7%
1 9395
 
13.0%
/ 6613
 
9.2%
3 3367
 
4.7%
4 2902
 
4.0%
9 2292
 
3.2%
8 1218
 
1.7%
5 1045
 
1.4%
6 890
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72079
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 27147
37.7%
0 16349
22.7%
1 9395
 
13.0%
/ 6613
 
9.2%
3 3367
 
4.7%
4 2902
 
4.0%
9 2292
 
3.2%
8 1218
 
1.7%
5 1045
 
1.4%
6 890
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72079
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 27147
37.7%
0 16349
22.7%
1 9395
 
13.0%
/ 6613
 
9.2%
3 3367
 
4.7%
4 2902
 
4.0%
9 2292
 
3.2%
8 1218
 
1.7%
5 1045
 
1.4%
6 890
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72079
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 27147
37.7%
0 16349
22.7%
1 9395
 
13.0%
/ 6613
 
9.2%
3 3367
 
4.7%
4 2902
 
4.0%
9 2292
 
3.2%
8 1218
 
1.7%
5 1045
 
1.4%
6 890
 
1.2%

Mat
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6346095
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:13.036662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum24
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.3904633
Coefficient of variation (CV)0.73155318
Kurtosis2.6837647
Mean4.6346095
Median Absolute Deviation (MAD)2
Skewness1.4693552
Sum60528
Variance11.495241
MonotonicityNot monotonic
2024-11-06T19:55:13.506433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 2077
15.9%
3 2010
15.4%
1 1940
14.9%
4 1754
13.4%
5 1338
10.2%
6 1000
7.7%
7 706
 
5.4%
8 555
 
4.2%
9 452
 
3.5%
10 354
 
2.7%
Other values (14) 874
6.7%
ValueCountFrequency (%)
1 1940
14.9%
2 2077
15.9%
3 2010
15.4%
4 1754
13.4%
5 1338
10.2%
6 1000
7.7%
7 706
 
5.4%
8 555
 
4.2%
9 452
 
3.5%
10 354
 
2.7%
ValueCountFrequency (%)
24 5
 
< 0.1%
23 3
 
< 0.1%
22 3
 
< 0.1%
21 8
 
0.1%
20 14
 
0.1%
19 10
 
0.1%
18 30
0.2%
17 35
0.3%
16 50
0.4%
15 71
0.5%

Inns
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4040582
Minimum0
Maximum24
Zeros1336
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:13.989985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0671678
Coefficient of variation (CV)0.90103273
Kurtosis3.5372443
Mean3.4040582
Median Absolute Deviation (MAD)2
Skewness1.6421831
Sum44457
Variance9.4075186
MonotonicityNot monotonic
2024-11-06T19:55:14.487764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 2704
20.7%
2 2263
17.3%
3 1898
14.5%
4 1433
11.0%
0 1336
10.2%
5 1002
 
7.7%
6 672
 
5.1%
7 440
 
3.4%
8 354
 
2.7%
9 262
 
2.0%
Other values (15) 696
 
5.3%
ValueCountFrequency (%)
0 1336
10.2%
1 2704
20.7%
2 2263
17.3%
3 1898
14.5%
4 1433
11.0%
5 1002
 
7.7%
6 672
 
5.1%
7 440
 
3.4%
8 354
 
2.7%
9 262
 
2.0%
ValueCountFrequency (%)
24 1
 
< 0.1%
23 2
 
< 0.1%
22 1
 
< 0.1%
21 1
 
< 0.1%
20 4
 
< 0.1%
19 5
 
< 0.1%
18 15
 
0.1%
17 18
 
0.1%
16 29
0.2%
15 45
0.3%

Batting Runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct452
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.308806
Minimum0
Maximum749
Zeros2074
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:14.948784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median23
Q371
95-th percentile213
Maximum749
Range749
Interquartile range (IQR)67

Descriptive statistics

Standard deviation77.109394
Coefficient of variation (CV)1.4464664
Kurtosis8.9331327
Mean53.308806
Median Absolute Deviation (MAD)23
Skewness2.594246
Sum696213
Variance5945.8586
MonotonicityNot monotonic
2024-11-06T19:55:15.539683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2074
 
15.9%
1 488
 
3.7%
2 362
 
2.8%
4 290
 
2.2%
5 269
 
2.1%
3 264
 
2.0%
6 256
 
2.0%
7 231
 
1.8%
8 208
 
1.6%
9 194
 
1.5%
Other values (442) 8424
64.5%
ValueCountFrequency (%)
0 2074
15.9%
1 488
 
3.7%
2 362
 
2.8%
3 264
 
2.0%
4 290
 
2.2%
5 269
 
2.1%
6 256
 
2.0%
7 231
 
1.8%
8 208
 
1.6%
9 194
 
1.5%
ValueCountFrequency (%)
749 1
< 0.1%
669 1
< 0.1%
625 1
< 0.1%
612 1
< 0.1%
608 1
< 0.1%
598 1
< 0.1%
597 2
< 0.1%
595 1
< 0.1%
587 1
< 0.1%
575 1
< 0.1%

SR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3318
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.544076
Minimum0
Maximum600
Zeros2074
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:16.098201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median96.9
Q3127.27
95-th percentile175
Maximum600
Range600
Interquartile range (IQR)77.27

Descriptive statistics

Standard deviation57.822277
Coefficient of variation (CV)0.64574096
Kurtosis2.1342055
Mean89.544076
Median Absolute Deviation (MAD)36.43
Skewness0.42043334
Sum1169445.6
Variance3343.4158
MonotonicityNot monotonic
2024-11-06T19:55:16.606643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2074
 
15.9%
100 599
 
4.6%
50 331
 
2.5%
66.66 192
 
1.5%
33.33 174
 
1.3%
75 117
 
0.9%
133.33 111
 
0.8%
150 110
 
0.8%
200 96
 
0.7%
125 89
 
0.7%
Other values (3308) 9167
70.2%
ValueCountFrequency (%)
0 2074
15.9%
5.55 1
 
< 0.1%
5.88 1
 
< 0.1%
6.66 1
 
< 0.1%
7.14 3
 
< 0.1%
7.31 1
 
< 0.1%
8.33 5
 
< 0.1%
8.69 1
 
< 0.1%
9.09 1
 
< 0.1%
10 5
 
< 0.1%
ValueCountFrequency (%)
600 3
 
< 0.1%
425 1
 
< 0.1%
400 14
0.1%
360 1
 
< 0.1%
350 6
 
< 0.1%
333.33 5
 
< 0.1%
328.57 1
 
< 0.1%
314.28 1
 
< 0.1%
306.66 1
 
< 0.1%
300 17
0.1%

Ave
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1299
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.814848
Minimum0
Maximum224
Zeros2074
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:17.135856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median10.33
Q321.4625
95-th percentile45.33
Maximum224
Range224
Interquartile range (IQR)18.9625

Descriptive statistics

Standard deviation16.656154
Coefficient of variation (CV)1.1242878
Kurtosis11.852862
Mean14.814848
Median Absolute Deviation (MAD)8.67
Skewness2.4678281
Sum193481.92
Variance277.42746
MonotonicityNot monotonic
2024-11-06T19:55:17.670808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2074
 
15.9%
1 484
 
3.7%
2 390
 
3.0%
4 294
 
2.3%
3 293
 
2.2%
5 276
 
2.1%
6 243
 
1.9%
7 228
 
1.7%
8 200
 
1.5%
11 192
 
1.5%
Other values (1289) 8386
64.2%
ValueCountFrequency (%)
0 2074
15.9%
0.25 1
 
< 0.1%
0.33 10
 
0.1%
0.5 86
 
0.7%
0.6 1
 
< 0.1%
0.66 12
 
0.1%
0.67 1
 
< 0.1%
0.75 4
 
< 0.1%
1 484
 
3.7%
1.2 2
 
< 0.1%
ValueCountFrequency (%)
224 1
< 0.1%
213 1
< 0.1%
211 1
< 0.1%
155 1
< 0.1%
152 1
< 0.1%
142 1
< 0.1%
138.33 1
< 0.1%
136 1
< 0.1%
134 1
< 0.1%
132 1
< 0.1%
Distinct97
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size836.3 KiB
2024-11-06T19:55:18.421843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length14
Mean length8.5653905
Min length4

Characters and Unicode

Total characters111864
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBulgaria
2nd rowBulgaria
3rd rowBulgaria
4th rowBulgaria
5th rowBulgaria
ValueCountFrequency (%)
new 715
 
4.2%
australia 580
 
3.4%
west 568
 
3.4%
indies 568
 
3.4%
india 567
 
3.3%
zealand 566
 
3.3%
south 563
 
3.3%
pakistan 560
 
3.3%
england 560
 
3.3%
africa 545
 
3.2%
Other values (103) 11162
65.8%
2024-11-06T19:55:19.672311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 17992
16.1%
n 9990
 
8.9%
i 8193
 
7.3%
e 7978
 
7.1%
t 5114
 
4.6%
d 5074
 
4.5%
r 5053
 
4.5%
l 4947
 
4.4%
s 4827
 
4.3%
3894
 
3.5%
Other values (41) 38802
34.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 111864
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 17992
16.1%
n 9990
 
8.9%
i 8193
 
7.3%
e 7978
 
7.1%
t 5114
 
4.6%
d 5074
 
4.5%
r 5053
 
4.5%
l 4947
 
4.4%
s 4827
 
4.3%
3894
 
3.5%
Other values (41) 38802
34.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 111864
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 17992
16.1%
n 9990
 
8.9%
i 8193
 
7.3%
e 7978
 
7.1%
t 5114
 
4.6%
d 5074
 
4.5%
r 5053
 
4.5%
l 4947
 
4.4%
s 4827
 
4.3%
3894
 
3.5%
Other values (41) 38802
34.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 111864
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 17992
16.1%
n 9990
 
8.9%
i 8193
 
7.3%
e 7978
 
7.1%
t 5114
 
4.6%
d 5074
 
4.5%
r 5053
 
4.5%
l 4947
 
4.4%
s 4827
 
4.3%
3894
 
3.5%
Other values (41) 38802
34.7%

Cum batting Ave
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3304
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.37357
Minimum0
Maximum126
Zeros984
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:20.214311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median13.4
Q322.8625
95-th percentile38
Maximum126
Range126
Interquartile range (IQR)17.8625

Descriptive statistics

Standard deviation12.755169
Coefficient of variation (CV)0.82968162
Kurtosis3.6400642
Mean15.37357
Median Absolute Deviation (MAD)8.73
Skewness1.3207957
Sum200778.83
Variance162.69433
MonotonicityNot monotonic
2024-11-06T19:55:20.774115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 984
 
7.5%
1 318
 
2.4%
2 229
 
1.8%
4 180
 
1.4%
3 177
 
1.4%
5 170
 
1.3%
6 143
 
1.1%
7 136
 
1.0%
8 102
 
0.8%
10 96
 
0.7%
Other values (3294) 10525
80.6%
ValueCountFrequency (%)
0 984
7.5%
0.17 1
 
< 0.1%
0.2 1
 
< 0.1%
0.25 5
 
< 0.1%
0.29 1
 
< 0.1%
0.33 14
 
0.1%
0.38 1
 
< 0.1%
0.4 4
 
< 0.1%
0.43 1
 
< 0.1%
0.44 1
 
< 0.1%
ValueCountFrequency (%)
126 1
< 0.1%
117 1
< 0.1%
115 1
< 0.1%
106.3 1
< 0.1%
104 1
< 0.1%
98 2
< 0.1%
96 2
< 0.1%
92 1
< 0.1%
90.27 1
< 0.1%
89.5 1
< 0.1%

Cum battings Runs Total
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1487
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232.63714
Minimum0
Maximum4231
Zeros984
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:21.334351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median69
Q3255
95-th percentile1071.05
Maximum4231
Range4231
Interquartile range (IQR)241

Descriptive statistics

Standard deviation412.61496
Coefficient of variation (CV)1.7736418
Kurtosis16.538651
Mean232.63714
Median Absolute Deviation (MAD)66
Skewness3.4813745
Sum3038241
Variance170251.1
MonotonicityNot monotonic
2024-11-06T19:55:21.912415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 984
 
7.5%
1 310
 
2.4%
2 269
 
2.1%
5 198
 
1.5%
3 181
 
1.4%
8 177
 
1.4%
7 169
 
1.3%
4 163
 
1.2%
6 160
 
1.2%
10 136
 
1.0%
Other values (1477) 10313
79.0%
ValueCountFrequency (%)
0 984
7.5%
1 310
 
2.4%
2 269
 
2.1%
3 181
 
1.4%
4 163
 
1.2%
5 198
 
1.5%
6 160
 
1.2%
7 169
 
1.3%
8 177
 
1.4%
9 107
 
0.8%
ValueCountFrequency (%)
4231 1
< 0.1%
4188 1
< 0.1%
4145 1
< 0.1%
4037 1
< 0.1%
4008 1
< 0.1%
3974 1
< 0.1%
3853 1
< 0.1%
3698 1
< 0.1%
3694 1
< 0.1%
3660 1
< 0.1%

Cum Inns Total
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct128
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.471822
Minimum0
Maximum151
Zeros602
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:22.465929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median7
Q317
95-th percentile50
Maximum151
Range151
Interquartile range (IQR)14

Descriptive statistics

Standard deviation17.07756
Coefficient of variation (CV)1.2676503
Kurtosis8.3439984
Mean13.471822
Median Absolute Deviation (MAD)5
Skewness2.5413848
Sum175942
Variance291.64305
MonotonicityNot monotonic
2024-11-06T19:55:23.075462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1269
 
9.7%
2 1068
 
8.2%
3 986
 
7.5%
4 886
 
6.8%
5 714
 
5.5%
6 621
 
4.8%
0 602
 
4.6%
7 599
 
4.6%
8 484
 
3.7%
9 421
 
3.2%
Other values (118) 5410
41.4%
ValueCountFrequency (%)
0 602
4.6%
1 1269
9.7%
2 1068
8.2%
3 986
7.5%
4 886
6.8%
5 714
5.5%
6 621
4.8%
7 599
4.6%
8 484
 
3.7%
9 421
 
3.2%
ValueCountFrequency (%)
151 1
< 0.1%
146 1
< 0.1%
144 1
< 0.1%
143 1
< 0.1%
140 1
< 0.1%
136 1
< 0.1%
131 1
< 0.1%
130 2
< 0.1%
127 2
< 0.1%
123 1
< 0.1%

Cum SR
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6497
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.052777
Minimum0
Maximum600
Zeros984
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:23.712753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q155.86
median93.59
Q3118.47
95-th percentile152.201
Maximum600
Range600
Interquartile range (IQR)62.61

Descriptive statistics

Standard deviation45.849254
Coefficient of variation (CV)0.52668341
Kurtosis2.4095615
Mean87.052777
Median Absolute Deviation (MAD)29.485
Skewness0.1120925
Sum1136909.3
Variance2102.1541
MonotonicityNot monotonic
2024-11-06T19:55:24.571112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 984
 
7.5%
100 216
 
1.7%
50 214
 
1.6%
33.33 106
 
0.8%
25 100
 
0.8%
66.66 69
 
0.5%
75 67
 
0.5%
16.67 61
 
0.5%
60 41
 
0.3%
37.5 39
 
0.3%
Other values (6487) 11163
85.5%
ValueCountFrequency (%)
0 984
7.5%
2.04 1
 
< 0.1%
2.38 1
 
< 0.1%
2.5 1
 
< 0.1%
2.86 1
 
< 0.1%
2.94 1
 
< 0.1%
3.57 2
 
< 0.1%
4.17 1
 
< 0.1%
4.76 3
 
< 0.1%
5 4
 
< 0.1%
ValueCountFrequency (%)
600 2
< 0.1%
400 1
< 0.1%
333.33 1
< 0.1%
328.85 1
< 0.1%
300 2
< 0.1%
280 1
< 0.1%
278.57 1
< 0.1%
270.66 1
< 0.1%
266.66 2
< 0.1%
260 1
< 0.1%
Distinct3559
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Memory size829.1 KiB
2024-11-06T19:55:25.507105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters104480
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1072 ?
Unique (%)8.2%

Sample

1st row55a5cffb
2nd row55a5cffb
3rd row55a5cffb
4th row55a5cffb
5th row55a5cffb
ValueCountFrequency (%)
bf1d7d3e 25
 
0.2%
99b75528 25
 
0.2%
740742ef 25
 
0.2%
d2a6c0e6 25
 
0.2%
7dc35884 24
 
0.2%
dcce6f09 23
 
0.2%
9ab63e7b 23
 
0.2%
13c35c9e 23
 
0.2%
ba607b88 23
 
0.2%
a94e08ea 23
 
0.2%
Other values (3549) 12821
98.2%
2024-11-06T19:55:26.870711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
d 6829
 
6.5%
e 6788
 
6.5%
b 6787
 
6.5%
a 6723
 
6.4%
8 6592
 
6.3%
1 6579
 
6.3%
2 6560
 
6.3%
f 6548
 
6.3%
3 6502
 
6.2%
9 6492
 
6.2%
Other values (6) 38080
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 104480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 6829
 
6.5%
e 6788
 
6.5%
b 6787
 
6.5%
a 6723
 
6.4%
8 6592
 
6.3%
1 6579
 
6.3%
2 6560
 
6.3%
f 6548
 
6.3%
3 6502
 
6.2%
9 6492
 
6.2%
Other values (6) 38080
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 104480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 6829
 
6.5%
e 6788
 
6.5%
b 6787
 
6.5%
a 6723
 
6.4%
8 6592
 
6.3%
1 6579
 
6.3%
2 6560
 
6.3%
f 6548
 
6.3%
3 6502
 
6.2%
9 6492
 
6.2%
Other values (6) 38080
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 104480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 6829
 
6.5%
e 6788
 
6.5%
b 6787
 
6.5%
a 6723
 
6.4%
8 6592
 
6.3%
1 6579
 
6.3%
2 6560
 
6.3%
f 6548
 
6.3%
3 6502
 
6.2%
9 6492
 
6.2%
Other values (6) 38080
36.4%

Overs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct339
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6482466
Minimum0
Maximum79.4
Zeros4706
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:27.430137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q312
95-th percentile29
Maximum79.4
Range79.4
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.228883
Coefficient of variation (CV)1.3374154
Kurtosis4.578812
Mean7.6482466
Median Absolute Deviation (MAD)4
Skewness1.9416603
Sum99886.1
Variance104.63005
MonotonicityNot monotonic
2024-11-06T19:55:27.980158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4706
36.0%
4 763
 
5.8%
1 480
 
3.7%
2 467
 
3.6%
3 458
 
3.5%
8 445
 
3.4%
7 343
 
2.6%
6 334
 
2.6%
12 313
 
2.4%
5 270
 
2.1%
Other values (329) 4481
34.3%
ValueCountFrequency (%)
0 4706
36.0%
0.1 13
 
0.1%
0.2 11
 
0.1%
0.3 7
 
0.1%
0.4 9
 
0.1%
0.5 10
 
0.1%
1 480
 
3.7%
1.1 10
 
0.1%
1.2 14
 
0.1%
1.3 7
 
0.1%
ValueCountFrequency (%)
79.4 1
< 0.1%
74 1
< 0.1%
73.4 1
< 0.1%
73 1
< 0.1%
68.5 1
< 0.1%
68.1 1
< 0.1%
68 1
< 0.1%
67.4 1
< 0.1%
66.5 1
< 0.1%
65 1
< 0.1%

Bowling_Runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct400
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.188821
Minimum0
Maximum559
Zeros4705
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:28.561926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median29
Q386
95-th percentile208
Maximum559
Range559
Interquartile range (IQR)86

Descriptive statistics

Standard deviation73.643072
Coefficient of variation (CV)1.3106357
Kurtosis4.3593706
Mean56.188821
Median Absolute Deviation (MAD)29
Skewness1.8976227
Sum733826
Variance5423.3021
MonotonicityNot monotonic
2024-11-06T19:55:29.092592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4705
36.0%
26 89
 
0.7%
37 87
 
0.7%
22 87
 
0.7%
19 84
 
0.6%
16 84
 
0.6%
29 83
 
0.6%
24 83
 
0.6%
48 83
 
0.6%
25 82
 
0.6%
Other values (390) 7593
58.1%
ValueCountFrequency (%)
0 4705
36.0%
1 17
 
0.1%
2 13
 
0.1%
3 17
 
0.1%
4 29
 
0.2%
5 38
 
0.3%
6 28
 
0.2%
7 47
 
0.4%
8 55
 
0.4%
9 58
 
0.4%
ValueCountFrequency (%)
559 1
 
< 0.1%
548 1
 
< 0.1%
521 3
< 0.1%
486 1
 
< 0.1%
485 1
 
< 0.1%
468 1
 
< 0.1%
460 1
 
< 0.1%
458 1
 
< 0.1%
457 2
< 0.1%
450 1
 
< 0.1%

Wkts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4974732
Minimum0
Maximum41
Zeros6260
Zeros (%)47.9%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:29.567948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile11
Maximum41
Range41
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.8646497
Coefficient of variation (CV)1.5474239
Kurtosis7.5107726
Mean2.4974732
Median Absolute Deviation (MAD)1
Skewness2.3545552
Sum32617
Variance14.935517
MonotonicityNot monotonic
2024-11-06T19:55:30.042724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 6260
47.9%
1 1371
 
10.5%
2 1142
 
8.7%
3 944
 
7.2%
4 683
 
5.2%
5 540
 
4.1%
6 436
 
3.3%
7 337
 
2.6%
8 284
 
2.2%
9 219
 
1.7%
Other values (24) 844
 
6.5%
ValueCountFrequency (%)
0 6260
47.9%
1 1371
 
10.5%
2 1142
 
8.7%
3 944
 
7.2%
4 683
 
5.2%
5 540
 
4.1%
6 436
 
3.3%
7 337
 
2.6%
8 284
 
2.2%
9 219
 
1.7%
ValueCountFrequency (%)
41 1
 
< 0.1%
38 1
 
< 0.1%
33 1
 
< 0.1%
31 1
 
< 0.1%
29 1
 
< 0.1%
28 1
 
< 0.1%
27 7
0.1%
26 7
0.1%
25 4
< 0.1%
24 2
 
< 0.1%

Econ
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct892
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0849112
Minimum0
Maximum39
Zeros4705
Zeros (%)36.0%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:30.544077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q38.1
95-th percentile11.66
Maximum39
Range39
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation4.4324468
Coefficient of variation (CV)0.87168617
Kurtosis0.58918853
Mean5.0849112
Median Absolute Deviation (MAD)3.41
Skewness0.46064455
Sum66408.94
Variance19.646584
MonotonicityNot monotonic
2024-11-06T19:55:31.098742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4705
36.0%
8 212
 
1.6%
7 208
 
1.6%
6 199
 
1.5%
9 197
 
1.5%
10 132
 
1.0%
11 117
 
0.9%
5 116
 
0.9%
6.5 100
 
0.8%
7.5 91
 
0.7%
Other values (882) 6983
53.5%
ValueCountFrequency (%)
0 4705
36.0%
0.5 1
 
< 0.1%
0.54 1
 
< 0.1%
1 9
 
0.1%
1.33 1
 
< 0.1%
1.5 3
 
< 0.1%
1.63 1
 
< 0.1%
1.71 1
 
< 0.1%
1.85 1
 
< 0.1%
2 10
 
0.1%
ValueCountFrequency (%)
39 1
 
< 0.1%
36 1
 
< 0.1%
33 1
 
< 0.1%
32 2
 
< 0.1%
28 2
 
< 0.1%
27.5 1
 
< 0.1%
27 2
 
< 0.1%
25.2 1
 
< 0.1%
25 4
< 0.1%
24 8
0.1%

Cumulative Overs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1662
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.19438
Minimum0
Maximum458.1
Zeros3742
Zeros (%)28.7%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:31.592724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q338
95-th percentile131.21
Maximum458.1
Range458.1
Interquartile range (IQR)38

Descriptive statistics

Standard deviation49.08849
Coefficient of variation (CV)1.6257493
Kurtosis11.020278
Mean30.19438
Median Absolute Deviation (MAD)10
Skewness2.8799571
Sum394338.6
Variance2409.6799
MonotonicityNot monotonic
2024-11-06T19:55:32.143919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3742
28.7%
1 397
 
3.0%
4 350
 
2.7%
2 305
 
2.3%
3 248
 
1.9%
8 197
 
1.5%
6 191
 
1.5%
5 185
 
1.4%
7 179
 
1.4%
11 179
 
1.4%
Other values (1652) 7087
54.3%
ValueCountFrequency (%)
0 3742
28.7%
0.1 22
 
0.2%
0.2 16
 
0.1%
0.3 2
 
< 0.1%
0.4 4
 
< 0.1%
0.5 8
 
0.1%
1 397
 
3.0%
1.1 10
 
0.1%
1.2 20
 
0.2%
1.3 3
 
< 0.1%
ValueCountFrequency (%)
458.1 1
< 0.1%
456.5 1
< 0.1%
446.1 1
< 0.1%
421.5 1
< 0.1%
414.8 1
< 0.1%
414.5 1
< 0.1%
408.2 1
< 0.1%
395.1 1
< 0.1%
388.7 1
< 0.1%
379 1
< 0.1%

Cumulative Wkts
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct131
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8322358
Minimum0
Maximum164
Zeros4750
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:32.618773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q312
95-th percentile44
Maximum164
Range164
Interquartile range (IQR)12

Descriptive statistics

Standard deviation16.579015
Coefficient of variation (CV)1.6861897
Kurtosis11.472463
Mean9.8322358
Median Absolute Deviation (MAD)3
Skewness2.9400218
Sum128409
Variance274.86373
MonotonicityNot monotonic
2024-11-06T19:55:33.149040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4750
36.4%
1 879
 
6.7%
2 673
 
5.2%
3 600
 
4.6%
4 526
 
4.0%
5 450
 
3.4%
6 393
 
3.0%
7 351
 
2.7%
8 287
 
2.2%
9 277
 
2.1%
Other values (121) 3874
29.7%
ValueCountFrequency (%)
0 4750
36.4%
1 879
 
6.7%
2 673
 
5.2%
3 600
 
4.6%
4 526
 
4.0%
5 450
 
3.4%
6 393
 
3.0%
7 351
 
2.7%
8 287
 
2.2%
9 277
 
2.1%
ValueCountFrequency (%)
164 1
< 0.1%
157 1
< 0.1%
149 1
< 0.1%
144 1
< 0.1%
140 1
< 0.1%
138 1
< 0.1%
136 1
< 0.1%
135 1
< 0.1%
134 1
< 0.1%
132 2
< 0.1%

Cumulative Runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1379
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.57933
Minimum0
Maximum3671
Zeros3741
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:33.700425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median75
Q3278
95-th percentile971
Maximum3671
Range3671
Interquartile range (IQR)278

Descriptive statistics

Standard deviation359.30515
Coefficient of variation (CV)1.6215644
Kurtosis12.161453
Mean221.57933
Median Absolute Deviation (MAD)75
Skewness2.973318
Sum2893826
Variance129100.19
MonotonicityNot monotonic
2024-11-06T19:55:34.289934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3741
28.6%
11 72
 
0.6%
22 63
 
0.5%
15 61
 
0.5%
25 58
 
0.4%
12 58
 
0.4%
17 57
 
0.4%
8 53
 
0.4%
27 52
 
0.4%
14 51
 
0.4%
Other values (1369) 8794
67.3%
ValueCountFrequency (%)
0 3741
28.6%
1 20
 
0.2%
2 13
 
0.1%
3 22
 
0.2%
4 29
 
0.2%
5 43
 
0.3%
6 17
 
0.1%
7 26
 
0.2%
8 53
 
0.4%
9 34
 
0.3%
ValueCountFrequency (%)
3671 1
< 0.1%
3635 1
< 0.1%
3402 1
< 0.1%
3179 1
< 0.1%
3171 1
< 0.1%
3117 1
< 0.1%
3048 1
< 0.1%
2994 1
< 0.1%
2869 1
< 0.1%
2854 1
< 0.1%

Cumulative Inns
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct111
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7745023
Minimum0
Maximum126
Zeros3741
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:34.862418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q313
95-th percentile41
Maximum126
Range126
Interquartile range (IQR)13

Descriptive statistics

Standard deviation14.799181
Coefficient of variation (CV)1.5140598
Kurtosis8.483
Mean9.7745023
Median Absolute Deviation (MAD)4
Skewness2.5818444
Sum127655
Variance219.01577
MonotonicityNot monotonic
2024-11-06T19:55:35.433672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3741
28.6%
1 1067
 
8.2%
2 831
 
6.4%
3 712
 
5.5%
4 596
 
4.6%
5 508
 
3.9%
6 432
 
3.3%
7 372
 
2.8%
8 361
 
2.8%
9 361
 
2.8%
Other values (101) 4079
31.2%
ValueCountFrequency (%)
0 3741
28.6%
1 1067
 
8.2%
2 831
 
6.4%
3 712
 
5.5%
4 596
 
4.6%
5 508
 
3.9%
6 432
 
3.3%
7 372
 
2.8%
8 361
 
2.8%
9 361
 
2.8%
ValueCountFrequency (%)
126 1
 
< 0.1%
123 1
 
< 0.1%
120 2
< 0.1%
115 1
 
< 0.1%
113 2
< 0.1%
112 3
< 0.1%
107 1
 
< 0.1%
105 2
< 0.1%
103 1
 
< 0.1%
102 3
< 0.1%

Cumulative Econ
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2899
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.214006
Minimum0
Maximum64
Zeros3741
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:36.220973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18.06167
Q323.8
95-th percentile31
Maximum64
Range64
Interquartile range (IQR)23.8

Descriptive statistics

Standard deviation11.252113
Coefficient of variation (CV)0.73958905
Kurtosis-1.0491106
Mean15.214006
Median Absolute Deviation (MAD)7.6526159
Skewness-0.13673601
Sum198694.92
Variance126.61004
MonotonicityNot monotonic
2024-11-06T19:55:36.771277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3741
28.6%
22 118
 
0.9%
24 118
 
0.9%
21 111
 
0.8%
19 110
 
0.8%
16 99
 
0.8%
20 97
 
0.7%
15 95
 
0.7%
23 94
 
0.7%
17 90
 
0.7%
Other values (2889) 8387
64.2%
ValueCountFrequency (%)
0 3741
28.6%
1 20
 
0.2%
1.5 10
 
0.1%
2 14
 
0.1%
2.5 2
 
< 0.1%
2.666666667 1
 
< 0.1%
3 12
 
0.1%
3.25 1
 
< 0.1%
3.5 2
 
< 0.1%
4 28
 
0.2%
ValueCountFrequency (%)
64 1
< 0.1%
60 2
< 0.1%
58 1
< 0.1%
57 1
< 0.1%
55 1
< 0.1%
54 1
< 0.1%
52 1
< 0.1%
51 1
< 0.1%
50 2
< 0.1%
49.33333333 1
< 0.1%

Dis
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8239663
Minimum0
Maximum102
Zeros2401
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:37.270496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile27
Maximum102
Range102
Interquartile range (IQR)7

Descriptive statistics

Standard deviation9.8990165
Coefficient of variation (CV)1.4506251
Kurtosis11.38489
Mean6.8239663
Median Absolute Deviation (MAD)3
Skewness2.9125681
Sum89121
Variance97.990527
MonotonicityNot monotonic
2024-11-06T19:55:37.865014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2401
18.4%
1 1952
14.9%
2 1406
10.8%
3 1095
 
8.4%
4 865
 
6.6%
5 705
 
5.4%
6 558
 
4.3%
7 467
 
3.6%
8 383
 
2.9%
9 303
 
2.3%
Other values (72) 2925
22.4%
ValueCountFrequency (%)
0 2401
18.4%
1 1952
14.9%
2 1406
10.8%
3 1095
8.4%
4 865
 
6.6%
5 705
 
5.4%
6 558
 
4.3%
7 467
 
3.6%
8 383
 
2.9%
9 303
 
2.3%
ValueCountFrequency (%)
102 1
< 0.1%
92 1
< 0.1%
91 1
< 0.1%
89 1
< 0.1%
87 1
< 0.1%
85 2
< 0.1%
78 1
< 0.1%
77 1
< 0.1%
76 1
< 0.1%
75 1
< 0.1%

Ct
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4640123
Minimum0
Maximum84
Zeros2435
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:38.423008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile25
Maximum84
Range84
Interquartile range (IQR)7

Descriptive statistics

Standard deviation9.0874628
Coefficient of variation (CV)1.4058548
Kurtosis9.4468669
Mean6.4640123
Median Absolute Deviation (MAD)3
Skewness2.7044963
Sum84420
Variance82.581981
MonotonicityNot monotonic
2024-11-06T19:55:38.935162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2435
18.6%
1 1976
15.1%
2 1430
10.9%
3 1084
 
8.3%
4 892
 
6.8%
5 707
 
5.4%
6 548
 
4.2%
7 477
 
3.7%
8 396
 
3.0%
10 298
 
2.3%
Other values (65) 2817
21.6%
ValueCountFrequency (%)
0 2435
18.6%
1 1976
15.1%
2 1430
10.9%
3 1084
8.3%
4 892
 
6.8%
5 707
 
5.4%
6 548
 
4.2%
7 477
 
3.7%
8 396
 
3.0%
9 292
 
2.2%
ValueCountFrequency (%)
84 1
 
< 0.1%
76 1
 
< 0.1%
74 1
 
< 0.1%
72 1
 
< 0.1%
71 1
 
< 0.1%
70 2
 
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
66 1
 
< 0.1%
65 7
0.1%

St
Real number (ℝ)

ZEROS 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35995406
Minimum0
Maximum34
Zeros12001
Zeros (%)91.9%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:39.400722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum34
Range34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9344661
Coefficient of variation (CV)5.3742028
Kurtosis110.8363
Mean0.35995406
Median Absolute Deviation (MAD)0
Skewness9.3525394
Sum4701
Variance3.7421591
MonotonicityNot monotonic
2024-11-06T19:55:39.886751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 12001
91.9%
1 305
 
2.3%
2 235
 
1.8%
3 112
 
0.9%
4 88
 
0.7%
5 62
 
0.5%
6 52
 
0.4%
7 44
 
0.3%
8 35
 
0.3%
9 26
 
0.2%
Other values (24) 100
 
0.8%
ValueCountFrequency (%)
0 12001
91.9%
1 305
 
2.3%
2 235
 
1.8%
3 112
 
0.9%
4 88
 
0.7%
5 62
 
0.5%
6 52
 
0.4%
7 44
 
0.3%
8 35
 
0.3%
9 26
 
0.2%
ValueCountFrequency (%)
34 1
 
< 0.1%
33 1
 
< 0.1%
32 2
 
< 0.1%
30 2
 
< 0.1%
29 4
< 0.1%
28 7
0.1%
27 2
 
< 0.1%
26 1
 
< 0.1%
25 2
 
< 0.1%
24 3
< 0.1%

D/I
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1106
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3378297
Minimum0
Maximum4
Zeros2401
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size102.2 KiB
2024-11-06T19:55:40.393690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13333333
median0.2962963
Q30.49206349
95-th percentile0.9375
Maximum4
Range4
Interquartile range (IQR)0.35873016

Descriptive statistics

Standard deviation0.29760334
Coefficient of variation (CV)0.88092711
Kurtosis8.1730296
Mean0.3378297
Median Absolute Deviation (MAD)0.1712963
Skewness1.8244842
Sum4412.0559
Variance0.088567748
MonotonicityNot monotonic
2024-11-06T19:55:40.997616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2401
 
18.4%
0.3333333333 694
 
5.3%
0.5 679
 
5.2%
0.25 488
 
3.7%
0.2 393
 
3.0%
1 373
 
2.9%
0.1666666667 289
 
2.2%
0.6666666667 245
 
1.9%
0.4 234
 
1.8%
0.1428571429 201
 
1.5%
Other values (1096) 7063
54.1%
ValueCountFrequency (%)
0 2401
18.4%
0.0243902439 1
 
< 0.1%
0.0303030303 1
 
< 0.1%
0.03125 1
 
< 0.1%
0.03225806452 1
 
< 0.1%
0.03333333333 1
 
< 0.1%
0.03703703704 2
 
< 0.1%
0.03846153846 4
 
< 0.1%
0.04347826087 3
 
< 0.1%
0.04545454545 3
 
< 0.1%
ValueCountFrequency (%)
4 1
 
< 0.1%
3 7
 
0.1%
2 33
0.3%
1.833333333 1
 
< 0.1%
1.75 1
 
< 0.1%
1.714285714 1
 
< 0.1%
1.666666667 8
 
0.1%
1.625 1
 
< 0.1%
1.615384615 1
 
< 0.1%
1.6 1
 
< 0.1%

Interactions

2024-11-06T19:54:58.548678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:17.790882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:24.743415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:31.685628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:38.786953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:46.247009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:54.767916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:04.971479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:13.655009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:22.415180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:29.924863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:37.322964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:44.464255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:52.060332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:59.754817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:06.838212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:14.509861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:21.525119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:28.781647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:35.281238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:41.823082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:49.947091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:58.948573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:18.205385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:25.002915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:31.970830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:39.129589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:46.609760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:55.387529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:05.479120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:14.297009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:22.748160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:30.256390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:37.595270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:44.806190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:52.392197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:00.014914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:07.191726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:14.839106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:21.833206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:29.060633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:35.547633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:42.156998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:50.286792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:59.357269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:18.495264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:25.347620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:32.254396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:39.690947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:46.879119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:55.834008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:05.990185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:14.662337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:23.041744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:30.579023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:37.886925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:45.123859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:52.685087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:00.364780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:07.506805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:15.130890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:22.089625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:29.397430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:35.820557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-11-06T19:55:06.810142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:24.451017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:31.366689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:38.391665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:45.884821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:52:54.377218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:04.474660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:13.290802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:21.996182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:29.631472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:36.952463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:44.113271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:51.718408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:53:59.339322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:06.489867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:14.139295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:21.170422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:28.427228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:34.935675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:41.503340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:49.534814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-11-06T19:54:58.137931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-11-06T19:55:41.502559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AveBatting RunsBowling_RunsCtCum Inns TotalCum SRCum batting AveCum battings Runs TotalCumulative EconCumulative InnsCumulative OversCumulative RunsCumulative WktsD/IDisEconInnsMatOversSRSeasonStWkts
Ave1.0000.911-0.2040.3620.4970.6900.8200.705-0.325-0.132-0.168-0.168-0.1600.2870.365-0.2530.6160.361-0.2020.7970.0280.149-0.174
Batting Runs0.9111.000-0.1660.4340.6060.6740.7800.775-0.345-0.112-0.155-0.155-0.1470.3080.437-0.2500.8420.579-0.1640.7340.0490.171-0.137
Bowling_Runs-0.204-0.1661.0000.029-0.042-0.159-0.280-0.2050.7660.7860.8140.8130.785-0.1710.0080.7260.0250.3430.987-0.0790.076-0.3140.886
Ct0.3620.4340.0291.0000.8300.3710.4720.730-0.0710.3210.2820.2800.2910.6780.996-0.1100.4290.4230.0350.3370.0670.2580.054
Cum Inns Total0.4970.606-0.0420.8301.0000.4990.6180.912-0.1410.2790.2280.2310.2300.3410.827-0.1570.6140.471-0.0390.4300.0840.217-0.022
Cum SR0.6900.674-0.1590.3710.4991.0000.7490.684-0.229-0.061-0.092-0.088-0.0860.2730.370-0.1870.4780.259-0.1600.7860.0420.108-0.136
Cum batting Ave0.8200.780-0.2800.4720.6180.7491.0000.854-0.348-0.108-0.151-0.150-0.1430.3360.474-0.2940.5350.274-0.2770.6400.0350.176-0.240
Cum battings Runs Total0.7050.775-0.2050.7300.9120.6840.8541.000-0.2890.0720.0190.0220.0240.3820.730-0.2610.6420.406-0.2020.5800.0570.230-0.171
Cumulative Econ-0.325-0.3450.766-0.071-0.141-0.229-0.348-0.2891.0000.7160.7630.7890.708-0.242-0.0920.741-0.239-0.0070.731-0.1800.079-0.3310.630
Cumulative Inns-0.132-0.1120.7860.3210.279-0.061-0.1080.0720.7161.0000.9930.9910.957-0.0840.2970.5960.0080.2400.789-0.0240.072-0.3240.702
Cumulative Overs-0.168-0.1550.8140.2820.228-0.092-0.1510.0190.7630.9931.0000.9950.968-0.1040.2580.602-0.0290.2200.820-0.0510.062-0.3270.735
Cumulative Runs-0.168-0.1550.8130.2800.231-0.088-0.1500.0220.7890.9910.9951.0000.958-0.1100.2560.632-0.0300.2140.808-0.0480.059-0.3280.718
Cumulative Wkts-0.160-0.1470.7850.2910.230-0.086-0.1430.0240.7080.9570.9680.9581.000-0.0860.2690.536-0.0260.2260.797-0.0440.058-0.3030.777
D/I0.2870.308-0.1710.6780.3410.2730.3360.382-0.242-0.084-0.104-0.110-0.0861.0000.695-0.2120.2530.172-0.1620.2400.0620.373-0.129
Dis0.3650.4370.0080.9960.8270.3700.4740.730-0.0920.2970.2580.2560.2690.6951.000-0.1300.4310.4200.0140.3360.0580.3200.036
Econ-0.253-0.2500.726-0.110-0.157-0.187-0.294-0.2610.7410.5960.6020.6320.536-0.212-0.1301.000-0.1470.0090.651-0.1390.023-0.3090.481
Inns0.6160.8420.0250.4290.6140.4780.5350.642-0.2390.008-0.029-0.030-0.0260.2530.431-0.1471.0000.7990.0260.4940.1230.1420.037
Mat0.3610.5790.3430.4230.4710.2590.2740.406-0.0070.2400.2200.2140.2260.1720.4200.0090.7991.0000.3500.3120.1590.0770.352
Overs-0.202-0.1640.9870.035-0.039-0.160-0.277-0.2020.7310.7890.8200.8080.797-0.1620.0140.6510.0260.3501.000-0.0820.074-0.3140.914
SR0.7970.734-0.0790.3370.4300.7860.6400.580-0.180-0.024-0.051-0.048-0.0440.2400.336-0.1390.4940.312-0.0821.0000.0500.101-0.063
Season0.0280.0490.0760.0670.0840.0420.0350.0570.0790.0720.0620.0590.0580.0620.0580.0230.1230.1590.0740.0501.0000.0250.115
St0.1490.171-0.3140.2580.2170.1080.1760.230-0.331-0.324-0.327-0.328-0.3030.3730.320-0.3090.1420.077-0.3140.1010.0251.000-0.269
Wkts-0.174-0.1370.8860.054-0.022-0.136-0.240-0.1710.6300.7020.7350.7180.777-0.1290.0360.4810.0370.3520.914-0.0630.115-0.2691.000

Missing values

2024-11-06T19:55:07.503932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-06T19:55:09.055822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PlayerSeasonMatInnsBatting RunsSRAveCountryCum batting AveCum battings Runs TotalCum Inns TotalCum SRplayer_idOversBowling_RunsWktsEconCumulative OversCumulative WktsCumulative RunsCumulative InnsCumulative EconDisCtStD/I
0A Ahmadhel2019/2032.016.0100.008.00Bulgaria8.0016.02.0100.0055a5cffb10.075.03.07.5010.03.075.03.025.0000000.00.00.00.000000
1A Ahmadhel202011.08.0100.008.00Bulgaria8.0024.03.0100.0055a5cffb2.022.01.011.0012.04.097.04.024.2500000.00.00.00.000000
2A Ahmadhel2020/2121.02.028.572.00Bulgaria6.5026.04.076.1955a5cffb2.427.02.010.1214.46.0124.06.020.6666670.00.00.00.000000
3A Ahmadhel202133.05.038.461.66Bulgaria4.4331.07.066.7655a5cffb3.031.00.010.3317.46.0155.08.019.3750000.00.00.00.000000
4A Ahmadhel202320.00.00.000.00Bulgaria4.4331.07.053.4155a5cffb0.00.00.00.0017.46.0155.08.019.3750000.00.00.00.000000
5A Ahmadhel202411.02.033.332.00Bulgaria4.1233.08.050.0655a5cffb0.00.00.00.0017.46.0155.08.019.3750001.01.00.00.090909
6A Amado202233.025.067.568.33Israel8.3325.03.067.5614ea33486.058.03.09.666.03.058.03.019.3333332.02.00.00.666667
7A Andrews2021/2231.00.00.000.00Switzerland0.000.01.00.001d45c01a10.061.04.06.1010.04.061.03.020.3333334.04.00.01.333333
8A Andrews202243.034.085.0017.00Switzerland12.7534.04.042.501d45c01a1.021.00.021.0011.04.082.04.020.5000007.07.00.01.000000
9A Ashok202310.00.00.000.00New Zealand0.000.00.00.00321be7e34.028.01.07.004.01.028.01.028.0000000.00.00.00.000000
PlayerSeasonMatInnsBatting RunsSRAveCountryCum batting AveCum battings Runs TotalCum Inns TotalCum SRplayer_idOversBowling_RunsWktsEconCumulative OversCumulative WktsCumulative RunsCumulative InnsCumulative EconDisCtStD/I
13050Zubaidi Zulkifle202488.0138.0117.9419.71Malaysia21.401183.059.0143.56dca8273d0.00.00.00.000.00.00.00.00.00000028.028.00.00.474576
13051Zuhaib Zubair2023/2421.013.0144.4413.00United Arab Emirates13.0013.01.0144.449124aa4c6.048.00.08.006.00.048.02.024.0000000.00.00.00.000000
13052Zuhair Muhammad2023/2422.07.043.753.50Saudi Arabia3.507.02.043.7522ae49730.00.00.00.000.00.00.00.00.0000000.00.00.00.000000
13053Zulfiqar Babar201332.024.0104.3412.00Pakistan12.0024.02.0104.343a0f6df212.081.07.06.7512.07.081.03.027.0000001.01.00.00.333333
13054Zulfiqar Babar2013/1441.03.075.003.00Pakistan9.0027.03.089.673a0f6df214.0104.05.07.4226.012.0185.07.026.4285711.01.00.00.142857
13055Zulqarnain Haider201941.00.00.000.00Spain0.000.01.00.002d46e8ed10.053.03.05.3010.03.053.04.013.2500001.01.00.00.250000
13056Zulqarnain Haider2019/2010.00.00.000.00Spain0.000.01.00.002d46e8ed3.07.00.02.3313.03.060.05.012.0000001.01.00.00.200000
13057Zulqarnain Haider202263.08.066.664.00Spain3.008.04.022.222d46e8ed15.091.05.06.0628.08.0151.011.013.7272731.01.00.00.090909
13058Zulqarnain Haider2006/0711.05.055.555.00Pakistan5.005.01.055.55ee9bdbc80.00.00.00.000.00.00.00.00.0000000.00.00.00.000000
13059Zulqarnain Haider2010/1122.018.085.719.00Pakistan7.6723.03.070.63ee9bdbc80.00.00.00.000.00.00.00.00.0000001.00.01.00.333333